Early Warning Urgent Logistic Disaster in Rural Area based on Classification Algorithm: Indonesian Case

According to the ESCAP report, nearly half of the world’s natural disasters occur in the Asia Pacific region, with Indonesia ranking first in terms of the number of victims. In terms of disasters, Indonesia lags in terms of early warning, response, and recovery. In terms of post-disaster management, the issue is the distribution of aid to disaster victims, including uneven logistics distribution, slow distribution, and poor coordination among related agencies. This paper proposes a solution in the form of a disaster logistics management system with data management features for disaster victims' needs, a donation collection system, and distribution management and monitoring of volunteers who will go to disaster sites. This system’s decision-making model is based on a smart system that uses sorting data to determine the priority level of the type of assistance needed at the disaster site and the K-Nearest Neighbor (KNN) algorithm to determine the priority of the distribution of aid locations. As a result, command center officers will be able to better coordinate with field teams in making decisions about priority actions that must be taken. Furthermore, this system serves as a public accountability medium for distributing disaster logistics to the government and related officials.